采用深度摄像头进行子计数估计的新方法,以支持大豆育种应用
Jithin Mathew1, Nadia Delavarpour1, Carrie Miranda2
1Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
一个深度摄像头通过去除背景杂乱来提高大豆计数的准确性. 这增强了像YOLOv7这样的深度学习模型,以更好地预测产量和作物管理.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 预测大豆产量对于粮食安全至关重要,需要非破坏性方法.
- 传统的子计数方法与作物背景干扰作斗争.
- 深度学习为自动化大豆探测提供了潜力.
研究的目的:
- 为了评估深度摄像头的实时RGB图像过在豆计数.
- 为了比较YOLOv7和YOLOv7-E6E对象检测模型用于大豆的分类.
- 评估背景删除对大豆产量估计深度学习模型性能的影响.
主要方法:
- 使用深度摄像头过RGB图像,使背景细分成为可能.
- 对象检测架构的YOLOv7和YOLOv7-E6E对象检测架构进行了比较.
- 训练和评估深度学习模型,带有和没有背景删除图像.
主要成果:
- 使用深度摄像头去除背景显著提高了YOLOv7的性能.
- 精度,回忆和mAP分数随着背景细分而大幅增加.
- 使用消除背景的YOLOv7实现了高性能指标 (mAP@0.5: 93.4%,mAP@0.5:0.95: 83.9%).
结论:
- 深度摄像头辅助的背景删除增强了基于深度学习的豆检测.
- YOLOv7是一个适合精确计数大豆的模型.
- 改进的豆检测有助于更可靠地预测大豆产量.
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